Overview

Dataset statistics

Number of variables12
Number of observations2191
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory205.5 KiB
Average record size in memory96.1 B

Variable types

DateTime1
Categorical1
Numeric10

Warnings

PM_RETIRO is highly correlated with PM_CIUDADLINEALHigh correlation
PM_CIUDADLINEAL is highly correlated with PM_RETIROHigh correlation
DEW_POINT is highly correlated with TEMPERATUREHigh correlation
TEMPERATURE is highly correlated with DEW_POINTHigh correlation
COMMULATIVE_PRECIPITATION is highly skewed (γ1 = 46.80810625) Skewed
FECHA has unique values Unique
PM_RETIRO has 1115 (50.9%) zeros Zeros
PM_VALLECAS has 1250 (57.1%) zeros Zeros
PM_CIUDADLINEAL has 1118 (51.0%) zeros Zeros
PM_CENTRO has 36 (1.6%) zeros Zeros
COMMULATIVE_PRECIPITATION has 1750 (79.9%) zeros Zeros

Reproduction

Analysis started2021-05-04 15:10:53.360340
Analysis finished2021-05-04 15:11:15.887157
Duration22.53 seconds
Software versionpandas-profiling v2.11.0
Download configurationconfig.yaml

Variables

FECHA
Date

UNIQUE

Distinct2191
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size17.2 KiB
Minimum2010-01-01 00:00:00
Maximum2015-12-31 00:00:00
2021-05-04T10:11:16.087620image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:11:16.420730image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

SEASON
Categorical

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size17.2 KiB
2
552 
1
552 
3
546 
4
541 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2191
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row4
3rd row4
4th row4
5th row4
ValueCountFrequency (%)
2552
25.2%
1552
25.2%
3546
24.9%
4541
24.7%
2021-05-04T10:11:17.180699image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-05-04T10:11:17.368198image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
1552
25.2%
2552
25.2%
3546
24.9%
4541
24.7%

Most occurring characters

ValueCountFrequency (%)
1552
25.2%
2552
25.2%
3546
24.9%
4541
24.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2191
100.0%

Most frequent character per category

ValueCountFrequency (%)
1552
25.2%
2552
25.2%
3546
24.9%
4541
24.7%

Most occurring scripts

ValueCountFrequency (%)
Common2191
100.0%

Most frequent character per script

ValueCountFrequency (%)
1552
25.2%
2552
25.2%
3546
24.9%
4541
24.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII2191
100.0%

Most frequent character per block

ValueCountFrequency (%)
1552
25.2%
2552
25.2%
3546
24.9%
4541
24.7%

PM_RETIRO
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct998
Distinct (%)45.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.92409122
Minimum0
Maximum564.7083333
Zeros1115
Zeros (%)50.9%
Memory size17.2 KiB
2021-05-04T10:11:17.994508image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q366.45833333
95-th percentile183.0528846
Maximum564.7083333
Range564.7083333
Interquartile range (IQR)66.45833333

Descriptive statistics

Standard deviation68.72708376
Coefficient of variation (CV)1.564678559
Kurtosis6.430955004
Mean43.92409122
Median Absolute Deviation (MAD)0
Skewness2.243863554
Sum96237.68387
Variance4723.412042
MonotocityNot monotonic
2021-05-04T10:11:18.296702image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01115
50.9%
31.8754
 
0.2%
14.3753
 
0.1%
26.565217393
 
0.1%
15.791666673
 
0.1%
18.833333333
 
0.1%
43.916666673
 
0.1%
29.6252
 
0.1%
64.708333332
 
0.1%
24.958333332
 
0.1%
Other values (988)1051
48.0%
ValueCountFrequency (%)
01115
50.9%
31
 
< 0.1%
3.2916666671
 
< 0.1%
3.5416666671
 
< 0.1%
3.5454545451
 
< 0.1%
ValueCountFrequency (%)
564.70833331
< 0.1%
488.91666671
< 0.1%
439.91666671
< 0.1%
416.66666671
< 0.1%
399.70833331
< 0.1%

PM_VALLECAS
Real number (ℝ≥0)

ZEROS

Distinct881
Distinct (%)40.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.66228327
Minimum0
Maximum593
Zeros1250
Zeros (%)57.1%
Memory size17.2 KiB
2021-05-04T10:11:18.611856image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q361.4682971
95-th percentile179.9089027
Maximum593
Range593
Interquartile range (IQR)61.4682971

Descriptive statistics

Standard deviation67.35235951
Coefficient of variation (CV)1.698146298
Kurtosis8.38567089
Mean39.66228327
Median Absolute Deviation (MAD)0
Skewness2.495710493
Sum86900.06264
Variance4536.340331
MonotocityNot monotonic
2021-05-04T10:11:18.981312image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01250
57.1%
101.04166673
 
0.1%
1123
 
0.1%
45.291666673
 
0.1%
553
 
0.1%
24.416666672
 
0.1%
116.70833332
 
0.1%
77.083333332
 
0.1%
22.3752
 
0.1%
63.8752
 
0.1%
Other values (871)919
41.9%
ValueCountFrequency (%)
01250
57.1%
4.7333333331
 
< 0.1%
4.91
 
< 0.1%
4.9583333331
 
< 0.1%
5.7826086961
 
< 0.1%
ValueCountFrequency (%)
5931
< 0.1%
518.0476191
< 0.1%
442.6251
< 0.1%
439.45833331
< 0.1%
397.58333331
< 0.1%

PM_CIUDADLINEAL
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct1003
Distinct (%)45.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.54700502
Minimum0
Maximum510.0434783
Zeros1118
Zeros (%)51.0%
Memory size17.2 KiB
2021-05-04T10:11:19.292359image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q364.91666667
95-th percentile185.1958333
Maximum510.0434783
Range510.0434783
Interquartile range (IQR)64.91666667

Descriptive statistics

Standard deviation69.05689528
Coefficient of variation (CV)1.585801256
Kurtosis6.382821052
Mean43.54700502
Median Absolute Deviation (MAD)0
Skewness2.288068084
Sum95411.488
Variance4768.854785
MonotocityNot monotonic
2021-05-04T10:11:19.575126image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01118
51.0%
71.583333333
 
0.1%
33.541666673
 
0.1%
23.666666673
 
0.1%
37.52
 
0.1%
23.583333332
 
0.1%
60.6252
 
0.1%
96.458333332
 
0.1%
57.708333332
 
0.1%
23.416666672
 
0.1%
Other values (993)1052
48.0%
ValueCountFrequency (%)
01118
51.0%
3.8421052631
 
< 0.1%
4.8571428571
 
< 0.1%
51
 
< 0.1%
5.8333333331
 
< 0.1%
ValueCountFrequency (%)
510.04347831
< 0.1%
466.79166671
< 0.1%
423.29166671
< 0.1%
417.46666671
< 0.1%
415.83333331
< 0.1%

PM_CENTRO
Real number (ℝ≥0)

ZEROS

Distinct1801
Distinct (%)82.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean94.3325817
Minimum0
Maximum568.5652174
Zeros36
Zeros (%)1.6%
Memory size17.2 KiB
2021-05-04T10:11:19.878318image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile13.70833333
Q137.5327381
median73.375
Q3124.6666667
95-th percentile250.3125
Maximum568.5652174
Range568.5652174
Interquartile range (IQR)87.13392857

Descriptive statistics

Standard deviation78.00042362
Coefficient of variation (CV)0.8268662027
Kurtosis3.759383961
Mean94.3325817
Median Absolute Deviation (MAD)41.5
Skewness1.680332444
Sum206682.6865
Variance6084.066085
MonotocityNot monotonic
2021-05-04T10:11:20.157569image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
036
 
1.6%
43.541666674
 
0.2%
22.041666674
 
0.2%
25.083333334
 
0.2%
14.416666673
 
0.1%
67.458333333
 
0.1%
43.416666673
 
0.1%
25.6253
 
0.1%
122.29166673
 
0.1%
54.6253
 
0.1%
Other values (1791)2125
97.0%
ValueCountFrequency (%)
036
1.6%
3.1818181821
 
< 0.1%
6.0833333331
 
< 0.1%
6.3333333331
 
< 0.1%
6.5416666671
 
< 0.1%
ValueCountFrequency (%)
568.56521741
< 0.1%
537.251
< 0.1%
492.751
< 0.1%
464.3751
< 0.1%
449.751
< 0.1%

DEW_POINT
Real number (ℝ)

HIGH CORRELATION

Distinct998
Distinct (%)45.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.074797095
Minimum-33.33333333
Maximum26.20833333
Zeros2
Zeros (%)0.1%
Memory size17.2 KiB
2021-05-04T10:11:20.544545image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-33.33333333
5-th percentile-20.29166667
Q1-9.75
median2.291666667
Q315.08333333
95-th percentile21.95833333
Maximum26.20833333
Range59.54166667
Interquartile range (IQR)24.83333333

Descriptive statistics

Standard deviation13.9572431
Coefficient of variation (CV)6.727040027
Kurtosis-1.218974946
Mean2.074797095
Median Absolute Deviation (MAD)12.45833333
Skewness-0.1381714527
Sum4545.880435
Variance194.8046351
MonotocityNot monotonic
2021-05-04T10:11:20.830292image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-9.757
 
0.3%
18.958333337
 
0.3%
16.791666677
 
0.3%
16.916666677
 
0.3%
207
 
0.3%
21.257
 
0.3%
14.916666676
 
0.3%
19.833333336
 
0.3%
22.256
 
0.3%
-7.256
 
0.3%
Other values (988)2125
97.0%
ValueCountFrequency (%)
-33.333333331
< 0.1%
-31.708333331
< 0.1%
-27.458333331
< 0.1%
-27.208333331
< 0.1%
-26.6251
< 0.1%
ValueCountFrequency (%)
26.208333332
0.1%
25.6251
< 0.1%
25.3751
< 0.1%
25.333333331
< 0.1%
25.251
< 0.1%

HUMIDITY
Real number (ℝ≥0)

Distinct1296
Distinct (%)59.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54.38462695
Minimum0
Maximum100
Zeros11
Zeros (%)0.5%
Memory size17.2 KiB
2021-05-04T10:11:21.101083image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile21.45833333
Q137.70833333
median55.04166667
Q370.89583333
95-th percentile86.70710784
Maximum100
Range100
Interquartile range (IQR)33.1875

Descriptive statistics

Standard deviation20.60985512
Coefficient of variation (CV)0.3789647235
Kurtosis-0.8250369418
Mean54.38462695
Median Absolute Deviation (MAD)16.5
Skewness-0.1122573834
Sum119156.7177
Variance424.766128
MonotocityNot monotonic
2021-05-04T10:11:21.423222image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
011
 
0.5%
496
 
0.3%
58.458333336
 
0.3%
80.1256
 
0.3%
57.041666676
 
0.3%
37.583333335
 
0.2%
40.583333335
 
0.2%
35.166666675
 
0.2%
67.041666675
 
0.2%
54.333333335
 
0.2%
Other values (1286)2131
97.3%
ValueCountFrequency (%)
011
0.5%
6.751
 
< 0.1%
8.3333333331
 
< 0.1%
8.9166666671
 
< 0.1%
91
 
< 0.1%
ValueCountFrequency (%)
1002
0.1%
991
< 0.1%
97.958333331
< 0.1%
96.958333331
< 0.1%
96.083333331
< 0.1%

PREASSURE
Real number (ℝ≥0)

Distinct852
Distinct (%)38.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1011.378024
Minimum0
Maximum1043.458333
Zeros11
Zeros (%)0.5%
Memory size17.2 KiB
2021-05-04T10:11:21.730400image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1000.4375
Q11007.875
median1016.25
Q31024.5625
95-th percentile1032.729167
Maximum1043.458333
Range1043.458333
Interquartile range (IQR)16.6875

Descriptive statistics

Standard deviation72.5619767
Coefficient of variation (CV)0.07174565296
Kurtosis187.0859274
Mean1011.378024
Median Absolute Deviation (MAD)8.375
Skewness-13.60847839
Sum2215929.25
Variance5265.240463
MonotocityNot monotonic
2021-05-04T10:11:22.012657image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
011
 
0.5%
1020.8758
 
0.4%
1007.8333338
 
0.4%
10148
 
0.4%
1019.1257
 
0.3%
1027.2083337
 
0.3%
1007.8757
 
0.3%
1002.0416677
 
0.3%
1015.9166677
 
0.3%
1020.6666677
 
0.3%
Other values (842)2114
96.5%
ValueCountFrequency (%)
011
0.5%
994.04166671
 
< 0.1%
994.45833332
 
0.1%
994.83333331
 
< 0.1%
994.95833331
 
< 0.1%
ValueCountFrequency (%)
1043.4583331
< 0.1%
1041.7083331
< 0.1%
1039.7083331
< 0.1%
1039.5833331
< 0.1%
1039.51
< 0.1%

TEMPERATURE
Real number (ℝ)

HIGH CORRELATION

Distinct1526
Distinct (%)69.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.89117975
Minimum-4.277322404
Maximum34.5204918
Zeros0
Zeros (%)0.0%
Memory size17.2 KiB
2021-05-04T10:11:22.307134image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-4.277322404
5-th percentile3.321721311
Q18.888661202
median19.08333333
Q326.63114754
95-th percentile30.45628415
Maximum34.5204918
Range38.79781421
Interquartile range (IQR)17.74248634

Descriptive statistics

Standard deviation9.385066363
Coefficient of variation (CV)0.5245638629
Kurtosis-1.306470141
Mean17.89117975
Median Absolute Deviation (MAD)8.538251366
Skewness-0.2101688121
Sum39199.57484
Variance88.07947064
MonotocityNot monotonic
2021-05-04T10:11:22.619342image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14.609289627
 
0.3%
27.314207657
 
0.3%
28.475409846
 
0.3%
21.849726785
 
0.2%
27.997267765
 
0.2%
3.5437158475
 
0.2%
29.090163935
 
0.2%
28.85109295
 
0.2%
9.8278688524
 
0.2%
21.064207654
 
0.2%
Other values (1516)2138
97.6%
ValueCountFrequency (%)
-4.2773224041
< 0.1%
-2.7062841531
< 0.1%
-2.6721311481
< 0.1%
-2.6038251371
< 0.1%
-2.0232240441
< 0.1%
ValueCountFrequency (%)
34.52049181
< 0.1%
34.110655741
< 0.1%
34.110655741
< 0.1%
33.564207652
0.1%
32.983606561
< 0.1%

WIND_SPEED
Real number (ℝ≥0)

Distinct2156
Distinct (%)98.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.26215979
Minimum1.244583333
Maximum463.1879167
Zeros0
Zeros (%)0.0%
Memory size17.2 KiB
2021-05-04T10:11:22.935302image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1.244583333
5-th percentile2.501875
Q15.679583333
median10.70875
Q321.58041667
95-th percentile84.67416667
Maximum463.1879167
Range461.9433333
Interquartile range (IQR)15.90083333

Descriptive statistics

Standard deviation41.02850686
Coefficient of variation (CV)1.76374452
Kurtosis29.34404446
Mean23.26215979
Median Absolute Deviation (MAD)6.113333333
Skewness4.769878438
Sum50967.3921
Variance1683.338375
MonotocityNot monotonic
2021-05-04T10:11:23.256525image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.97253
 
0.1%
6.052
 
0.1%
11.434166672
 
0.1%
5.3841666672
 
0.1%
11.196666672
 
0.1%
8.0841666672
 
0.1%
1.9341666672
 
0.1%
6.053752
 
0.1%
5.606252
 
0.1%
1.6552
 
0.1%
Other values (2146)2170
99.0%
ValueCountFrequency (%)
1.2445833331
< 0.1%
1.41251
< 0.1%
1.4845833331
< 0.1%
1.4866666671
< 0.1%
1.503751
< 0.1%
ValueCountFrequency (%)
463.18791671
< 0.1%
407.35333331
< 0.1%
384.42541671
< 0.1%
365.438751
< 0.1%
365.41166671
< 0.1%

COMMULATIVE_PRECIPITATION
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct179
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean458.2136011
Minimum0
Maximum999990
Zeros1750
Zeros (%)79.9%
Memory size17.2 KiB
2021-05-04T10:11:23.577179image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile10.9
Maximum999990
Range999990
Interquartile range (IQR)0

Descriptive statistics

Standard deviation21363.56483
Coefficient of variation (CV)46.62359385
Kurtosis2190.999207
Mean458.2136011
Median Absolute Deviation (MAD)0
Skewness46.80810625
Sum1003946
Variance456401902.4
MonotocityNot monotonic
2021-05-04T10:11:23.907297image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01750
79.9%
0.148
 
2.2%
0.231
 
1.4%
0.415
 
0.7%
0.610
 
0.5%
0.39
 
0.4%
0.89
 
0.4%
0.98
 
0.4%
0.78
 
0.4%
0.56
 
0.3%
Other values (169)297
 
13.6%
ValueCountFrequency (%)
01750
79.9%
0.148
 
2.2%
0.231
 
1.4%
0.39
 
0.4%
0.415
 
0.7%
ValueCountFrequency (%)
9999901
< 0.1%
2231
< 0.1%
203.61
< 0.1%
102.31
< 0.1%
75.81
< 0.1%

Interactions

2021-05-04T10:10:54.211207image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:10:54.545312image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:10:54.857629image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:10:55.149362image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:10:55.353816image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:10:55.545308image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:10:55.745799image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:10:55.961193image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:10:56.153677image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:10:56.357133image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:10:56.556221image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:10:56.793881image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:10:56.992176image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:10:57.200619image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:10:57.405089image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:10:57.601547image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:10:57.827942image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:10:58.026924image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:10:58.236369image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:10:58.427366image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:10:58.624349image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:10:58.820845image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:10:59.016357image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:10:59.350429image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:10:59.652624image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:11:00.132855image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:11:00.455896image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:11:00.656868image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:11:01.123178image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:11:01.327321image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:11:01.520348image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:11:01.722775image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:11:01.919282image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:11:02.116724image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:11:02.329664image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:11:02.518216image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:11:02.723497image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:11:02.942910image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:11:03.193243image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:11:03.422659image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:11:03.641045image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:11:03.862452image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:11:04.059054image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:11:04.301414image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:11:04.498450image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:11:04.698453image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:11:04.907442image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:11:05.151760image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:11:05.385134image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:11:05.629479image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:11:05.841507image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:11:06.031062image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:11:06.273451image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:11:06.476875image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:11:06.682225image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:11:06.890182image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:11:07.121108image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:11:07.320575image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:11:07.516051image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:11:07.706545image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:11:07.898995image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:11:08.110468image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:11:08.327367image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:11:08.529574image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:11:08.748989image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:11:08.972391image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:11:09.196821image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:11:09.421221image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:11:09.652647image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:11:09.957318image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:11:10.293958image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:11:10.601168image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:11:10.917292image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:11:11.126246image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:11:11.323723image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:11:11.514221image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:11:11.699231image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:11:11.888237image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:11:12.070718image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:11:12.268225image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:11:12.469654image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:11:12.670149image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:11:12.874638image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:11:13.082355image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:11:13.298807image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:11:13.535958image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:11:13.758356image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:11:13.958852image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:11:14.170831image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:11:14.439627image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2021-05-04T10:11:24.121727image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-05-04T10:11:24.594874image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-05-04T10:11:25.011873image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-05-04T10:11:25.441237image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-05-04T10:11:14.903244image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-05-04T10:11:15.605908image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

FECHASEASONPM_RETIROPM_VALLECASPM_CIUDADLINEALPM_CENTRODEW_POINTHUMIDITYPREASSURETEMPERATUREWIND_SPEEDCOMMULATIVE_PRECIPITATION
02010-01-0140.00.00.0129.000000-18.75000038.4583331017.0833332.04098414.4583330.0
12010-01-0240.00.00.0144.333333-8.50000077.9375001024.7500003.37295124.8600000.0
22010-01-0340.00.00.078.375000-10.12500087.9166671022.7916670.57240470.93791711.2
32010-01-0440.00.00.029.291667-20.87500046.2083331029.291667-1.852459111.1608330.0
42010-01-0540.00.00.043.541667-24.58333342.0416671033.625000-4.27732256.9200000.0
52010-01-0640.00.00.059.375000-23.70833339.2083331033.750000-2.70628418.5116670.0
62010-01-0740.00.00.072.458333-21.25000049.0000001034.083333-2.67213110.1700000.0
72010-01-0840.00.00.0174.333333-17.12500064.5416671028.000000-2.0232241.9729170.0
82010-01-0940.00.00.084.750000-16.33333357.2500001029.0416670.09426213.2987500.0
92010-01-1040.00.00.055.083333-15.95833356.5000001032.5000000.40163917.4158330.0

Last rows

FECHASEASONPM_RETIROPM_VALLECASPM_CIUDADLINEALPM_CENTRODEW_POINTHUMIDITYPREASSURETEMPERATUREWIND_SPEEDCOMMULATIVE_PRECIPITATION
21812015-12-224331.666667351.235294327.666667336.958333-4.41666789.1666671027.9583335.2172133.4791670.0
21822015-12-234275.041667257.217391250.125000254.541667-5.95833370.4583331026.5000007.2663936.2033330.0
21832015-12-244125.347826119.916667108.727273100.416667-6.75000064.2083331027.0000007.3346994.5041670.0
21842015-12-254564.708333518.047619510.043478537.250000-4.00000096.0833331019.2500004.7049182.2670830.0
21852015-12-264266.521739255.083333238.208333254.333333-5.04166786.5833331024.9166675.1147544.3012500.0
21862015-12-27452.79166766.04166757.70833356.208333-13.95833353.5416671038.6250002.9289623.9508330.0
21872015-12-284117.416667119.583333111.833333112.416667-11.45833360.7500001035.0416674.05601113.6566670.0
21882015-12-294323.416667361.500000330.750000331.875000-6.62500076.1250001028.8750005.2855191.2445830.0
21892015-12-30451.791667135.50000094.291667101.750000-8.75000058.4583331030.3750007.30054626.5025000.0
21902015-12-31463.82608783.16666761.30434870.875000-10.08333359.4166671032.4583335.2513669.0733330.0